Using dynamic triggers in dangerous situations to view sensor data for autonomous vehicle passengers

ABSTRACT

A method, non-transitory computer readable medium, and system for receiving sensor data from one or more sensors disposed on an autonomous vehicle, determining whether a potentially dangerous event is detected in the environment around the autonomous vehicle, and providing automatically at least a portion of the sensor data to a user associated with the autonomous vehicle. The sensor data may comprise measurements associated with an environment around the autonomous vehicle. The determination of the potentially dangerous event may be based on the sensor data. The portion of the sensor data may be provided automatically in response to determining that the potentially dangerous event is detected.

BACKGROUND 1. Technical Field

The present technology relates to using sensor data from autonomousvehicle (AV) sensors and more particularly to using AV sensor data todetect potentially dangerous situations.

2. Introduction

Autonomous vehicles (AVs) are vehicles having computers and controlsystems that perform driving and navigation tasks that areconventionally performed by a human driver. As AV technologies continueto advance, ride-sharing services will increasingly utilize AVs toimprove service efficiency and safety. However, for effective use inride-sharing deployments, AVs will be required to perform many of thefunctions that are conventionally performed by human drivers, such asreacting to potentially dangerous situations.

BRIEF DESCRIPTION OF THE DRAWINGS

The above-recited and other advantages and features of the presenttechnology will become apparent by reference to specific implementationsillustrated in the appended drawings. A person of ordinary skill in theart will understand that these drawings only show some examples of thepresent technology and would not limit the scope of the presenttechnology to these examples. Furthermore, the skilled artisan willappreciate the principles of the present technology as described andexplained with additional specificity and detail through the use of theaccompanying drawings in which:

FIG. 1 shows an example of a content management system and clientdevices;

FIG. 2 is a flow diagram that illustrates an example process for usingdynamic triggers to use sensor data; and

FIG. 3 shows an example of a system for implementing certain aspects ofthe present technology.

DETAILED DESCRIPTION

Various examples of the present technology are discussed in detailbelow. While specific implementations are discussed, it should beunderstood that this is done for illustration purposes only. A personskilled in the relevant art will recognize that other components andconfigurations may be used without parting from the spirit and scope ofthe present technology. In some instances, well-known structures anddevices are shown in block diagram form in order to facilitatedescribing one or more aspects. Further, it is to be understood thatfunctionality that is described as being carried out by certain systemcomponents may be performed by more or fewer components than shown.

An autonomous vehicle is a motorized vehicle that can navigate without ahuman driver. An exemplary autonomous vehicle includes a plurality ofsensor systems, such as, but not limited to, a camera sensor system, aLiDAR sensor system, a radar sensor system, amongst others, wherein theautonomous vehicle operates based upon sensor signals output by thesensor systems. Specifically, the sensor signals are provided to aninternal computing system in communication with the plurality of sensorsystems, wherein a processor executes instructions based upon the sensorsignals to control a mechanical system of the autonomous vehicle, suchas a vehicle propulsion system, a braking system, or a steering system.

Using sensor data is integral to the safe operation of an autonomousvehicle. At some point, a processor of the autonomous vehicle mayrecognize that a dangerous event has occurred by processing sensor datagathered from sensors of the autonomous vehicle. While human drivers mayuse their senses to determine that a dangerous situation has occurredand react accordingly, it is challenging for autonomous vehicles torecognize that a dangerous event has occurred. Furthermore, it is alsochallenging to determine how to react accordingly to the dangerousevent.

Using sensor data is integral to the safe operation of an autonomousvehicle. In traditional vehicles, human drivers are constantly usingtheir senses to determine when dangerous events may occur. Similarly,upon detection of a dangerous event, human drivers can use theirjudgment to determine how to respond. In some instances, human driversmay also not detect the dangerous event. In such instances, the humandrivers may not know how to respond.

For autonomous vehicles, it is challenging to determine when a dangerousevent occurs. Furthermore, it is also challenging for autonomousvehicles to determine how to react accordingly to the dangerous event.Thus, the disclosed technology addresses the need in the art for anefficient process for detecting dangerous events and determining stepsfor the autonomous vehicle to take in response to the detected dangerousevents. As will be discussed in further detail below, the autonomousvehicle may determine through various detected triggers that events maybe potentially dangerous. The autonomous vehicle may then escalate theseevents to passengers or to remote assistance providers. The responsesfrom the passengers or the remote assistance providers may then be usedto train machine learning implementations.

FIG. 1 illustrates environment 100 that includes an autonomous vehicle102 in communication with a remote computing system 150.

The autonomous vehicle 102 can navigate about roadways without a humandriver based upon sensor signals output by sensor systems 104-106 of theautonomous vehicle 102. The autonomous vehicle 102 includes a pluralityof sensor systems 104-106 (a first sensor system 104 through an Nthsensor system 106). The sensor systems 104-106 are of different typesand are arranged about the autonomous vehicle 102. For example, thefirst sensor system 104 may be a camera sensor system, and the Nthsensor system 106 may be a LiDAR sensor system. Other exemplary sensorsystems include radar sensor systems, global positioning system (GPS)sensor systems, inertial measurement units (IMU), infrared sensorsystems, laser sensor systems, sonar sensor systems, and the like.

The autonomous vehicle 102 further includes several mechanical systemsthat are used to effectuate appropriate motion of the autonomous vehicle102. For instance, the mechanical systems can include but are notlimited to, a vehicle propulsion system 130, a braking system 132, and asteering system 134. The vehicle propulsion system 130 may include anelectric motor, an internal combustion engine, or both. The brakingsystem 132 can include an engine brake, brake pads, actuators, and/orany other suitable componentry that is configured to assist indecelerating the autonomous vehicle 102. The steering system 134includes suitable componentry that is configured to control thedirection of movement of the autonomous vehicle 102 during navigation.

The autonomous vehicle 102 further includes a safety system 136 that caninclude various lights and signal indicators, parking brake, airbags,etc. The autonomous vehicle 102 further includes a cabin system 138 thatcan include cabin temperature control systems, in-cabin entertainmentsystems, etc.

The autonomous vehicle 102 additionally comprises an internal computingsystem 110 that is in communication with the sensor systems 104-106 andthe systems 130, 132, 134, 136, and 138. The internal computing systemincludes at least one processor and at least one memory havingcomputer-executable instructions that are executed by the processor. Thecomputer-executable instructions can make up one or more servicesresponsible for controlling the autonomous vehicle 102, communicatingwith remote computing system 150, receiving inputs from passengers orhuman co-pilots, logging metrics regarding data collected by sensorsystems 104-106 and human co-pilots, etc.

The internal computing system 110 can include a control service 112 thatis configured to control the operation of the vehicle propulsion system130, the braking system 132, the steering system 134, the safety system136, and the cabin system 138. The control service 112 receives sensorsignals from the sensor systems 104-106 as well communicates with otherservices of the internal computing system 110 to effectuate operation ofthe autonomous vehicle 102. In some embodiments, control service 112 maycarry out operations in concert one or more other systems of autonomousvehicle 102.

The internal computing system 110 can also include a constraint service114 to facilitate safe propulsion of the autonomous vehicle 102. Theconstraint service 114 includes instructions for activating a constraintbased on a rule-based restriction upon operation of the autonomousvehicle 102. For example, the constraint may be a restriction uponnavigation that is activated in accordance with protocols configured toavoid occupying the same space as other objects, abide by traffic laws,circumvent avoidance areas, etc. In some embodiments, the constraintservice can be part of the control service 112.

The internal computing system 110 can also include a communicationservice 116. The communication service can include both software andhardware elements for transmitting and receiving signals from/to theremote computing system 150. The communication service 116 is configuredto transmit information wirelessly over a network, for example, throughan antenna array that provides personal cellular (long-term evolution(LTE), 3G, 5G, etc.) communication.

In some embodiments, one or more services of the internal computingsystem 110 are configured to send and receive communications to remotecomputing system 150 for such reasons as reporting data for training andevaluating machine learning algorithms, requesting assistance fromremoting computing system or a human operator via remote computingsystem 150, software service updates, ridesharing pickup and drop offinstructions etc.

The internal computing system 110 can also include a latency service118. The latency service 118 can utilize timestamps on communications toand from the remote computing system 150 to determine if a communicationhas been received from the remote computing system 150 in time to beuseful. For example, when a service of the internal computing system 110requests feedback from remote computing system 150 on a time-sensitiveprocess, the latency service 118 can determine if a response was timelyreceived from remote computing system 150 as information can quicklybecome too stale to be actionable. When the latency service 118determines that a response has not been received within a threshold, thelatency service 118 can enable other systems of autonomous vehicle 102or a passenger to make necessary decisions or to provide the neededfeedback.

The internal computing system 110 can also include a user interfaceservice 120 that can communicate with cabin system 138 in order toprovide information or receive information to a human co-pilot or humanpassenger. In some embodiments, a human co-pilot or human passenger maybe required to evaluate and override a constraint from constraintservice 114, or the human co-pilot or human passenger may wish toprovide an instruction to the autonomous vehicle 102 regardingdestinations, requested routes, or other requested operations.

As described above, the remote computing system 150 is configured tosend/receive a signal from the autonomous vehicle 102 regardingreporting data for training and evaluating machine learning algorithms,requesting assistance from remote computing system 150 or a humanoperator via the remote computing system 150, software service updates,rideshare pickup and drop off instructions, etc.

The remote computing system 150 includes an analysis service 152 that isconfigured to receive data from autonomous vehicle 102 and analyze thedata to train or evaluate machine learning algorithms for operating theautonomous vehicle 102. The analysis service 152 can also performanalysis pertaining to data associated with one or more errors orconstraints reported by autonomous vehicle 102.

The remote computing system 150 can also include a user interfaceservice 154 configured to present metrics, video, pictures, soundsreported from the autonomous vehicle 102 to an operator of remotecomputing system 150. User interface service 154 can further receiveinput instructions from an operator that can be sent to the autonomousvehicle 102.

The remote computing system 150 can also include an instruction service156 for sending instructions regarding the operation of the autonomousvehicle 102. For example, in response to an output of the analysisservice 152 or user interface service 154, instructions service 156 canprepare instructions to one or more services of the autonomous vehicle102 or a co-pilot or passenger of the autonomous vehicle 102.

The remote computing system 150 can also include a rideshare service 158configured to interact with ridesharing application 170 operating on(potential) passenger computing devices. The rideshare service 158 canreceive requests to be picked up or dropped off from passengerridesharing app 170 and can dispatch autonomous vehicle 102 for thetrip. The rideshare service 158 can also act as an intermediary betweenthe ridesharing app 170 and the autonomous vehicle wherein a passengermight provide instructions to the autonomous vehicle to 102 go around anobstacle, change routes, honk the horn, etc.

As described herein, one aspect of the present technology is thegathering and use of data available from various sources to improvequality and experience. The present disclosure contemplates that in someinstances, this gathered data may include personal information. Thepresent disclosure contemplates that the entities involved with suchpersonal information respect and value privacy policies and practices.

FIG. 2 is a flow diagram that illustrates a process 200 for usingdynamic triggers to use sensor data. While this process 200 may beimplemented by various systems, this disclosure implements the process200 through the internal computing system 110 for clarity and discussionpurposes.

The process 200 begins with step 202, in which the internal computingsystem 110 receiving sensor data recorded or obtained by the sensorsystems 104-106 of the autonomous vehicle 102. The sensor data mayinclude measurements, images, and other data associated with theenvironment around the autonomous vehicle 102. As discussed above, thesensor systems 104-106 may include a camera sensor system capable ofcapturing an environment around the autonomous vehicle 102 as sensordata.

At step 204, the internal computing system 110 determines whether apotentially dangerous event is detected in the environment around theautonomous vehicle 102. More specifically, the internal computing system110 may detect dynamic triggers captured in the sensor data to determinewhether the potentially dangerous event has occurred. The dynamictriggers are events that may generally be associated with potentiallydangerous events. For example, the internal computing system 110 maydetect in sensor data captured by the camera sensor system a pedestrianoutside of the autonomous vehicle 102 lying on a sidewalk with blood.The pedestrian lying on the sidewalk may be a trigger that is associatedwith the potentially dangerous event of a crime. As another example, theinternal computing system 110 may detect in sensor data captured by amicrophone a gunshot sound. The internal computing system 110 may thendetermine that the gunshot, a trigger, is associated with a shooting, apotentially dangerous event. Similarly, the internal computing system110 may detect in sensor data captured by a camera a person approachingthe autonomous vehicle 102 with a weapon (e.g. a gun, knife,sledgehammer, etc.). Thus, the internal computing system 110 maydetermine that the weapon, a trigger, is associated with a crime, apotentially dangerous event. In other words, the internal computingsystem evaluates detected triggers to determine whether an event ispotentially dangerous.

In some embodiments, the potentially dangerous event may be anydangerous event on the road, involving any vehicle. In other words, thepotentially dangerous event is not limited to the autonomous vehicle102. For example, the internal computing system 110 may detect in sensordata captured by a camera a car accident occurring between two vehicles,neither of which are the autonomous vehicle 102.

In some embodiments, the internal computing system 110 may receive analert communicating that an event is the potentially dangerous event.Thus, the potentially dangerous event may be communicated to theinternal computing system 110. In other words, the internal computingsystem 110 may receive communicated data from some remote computingsystem. The internal computing system 110 may then further determinewhether a potentially dangerous event is detected based on thecommunicated data. For example, the internal computing system 110 mayreceive from a third party, such as a police department, an AMBER alertfor a specified license plate. Thus, the internal computing system 110may optically detect in sensor data captured by the camera sensor systemthe specified license plate, the trigger, and determine that the vehiclewith the specified license plate is a potentially dangerous event. Asanother example, the United States Geographical Survey regularlyprovides real-time data on earthquakes occurrences. The internalcomputing system 110 may receive communicated data in the form of anotice that an earthquake has occurred within the environment around theautonomous vehicle 102. The internal computing system 110 may thendetermine based on this communicated data that the potentially dangerousevent (i.e. the earthquake) has occurred. In some embodiments, theinternal computing system 110 may determine and/or verify, such asthrough a suspension system of the autonomous vehicle 102, that theearthquake has occurred. More specifically, the sensor data obtainedthrough the suspension system of the autonomous vehicle 102 may be usedto determine and/or verify that the earthquake has occurred within theenvironment around the autonomous vehicle 102. Similarly, the incomingor communicated data may be used as an additional verification layer toensure that the surrounding environment is safe for the passenger toexit the autonomous vehicle 102. For example, the autonomous vehicle 102may have first determine through sensor data obtained through thesuspension system of the autonomous vehicle 102 that an earthquake hasoccurred. The internal computing system 110 may then receive thecommunicated data indicating that the earthquake occurred, but nosignificant damage or danger exists. Thus, the internal computing system110 may determine whether the surrounding area is safe for the passengerto exit the autonomous vehicle 102.

If the internal computing system 110 determines that a potentiallydangerous event is not detected in the sensor data gathered by thesensor systems 104-106, then the process 200 returns to step 202, inwhich the internal computing system 110 continues receiving sensor datafrom the sensor systems 104-106.

If the internal computing system 110 determines that a potentiallydangerous event has been detected in the sensor data, then the process200 continues to step 206. At step 206, in some embodiments, theinternal computing system 110, in response to determining that thepotentially dangerous event is detected, determines a relevant sensor ofthe sensor systems 104-106. The relevant sensor is the sensor thatprovides the most relevant information about the detected potentiallydangerous event. For example, in the AMBER alert scenario above, therelevant sensor is the camera sensor system, not the microphone. Thus,the relevant sensor of the sensor systems 104-106 has gathered relevantsensor data associated with the potentially dangerous event.

Next, at step 208, the internal computing system 110 sends at least aportion of the sensor data to a user or passenger associated with theautonomous vehicle 102 to be displayed for the user or passenger to use.In some embodiments, the portion of the sensor data may be the relevantsensor data associated with the potentially dangerous event. Forexample, the internal computing system 110 may determine, based onimages or videos obtained by the camera system, a person is trying tovandalize the autonomous vehicle 102. The internal computing system 110may then send to the passenger, displaying via the user interfaceservice 120, the real-time sensor data observed by the camera system ofthe autonomous vehicle 102, so that the passenger is aware that apotentially dangerous event is occurring.

It is further contemplated that the internal computing system 110 mayaugment or provide further detail to the potentially dangerous events byutilizing the sensor data obtained by other sensor systems 104-106. Forexample, the internal computing system 110 may detect, based upon impactsensors and the camera systems of the autonomous vehicle 102, that animpact has occurred to the autonomous vehicle 102 (i.e. a trigger) andthat the impact was caused by another vehicle hitting the autonomousvehicle 102 (i.e. a potentially dangerous event). The internal computingsystem 110 may then send the images captured by the camera system to thepassenger and depict, based on a computer generated projection, theimpact location on the images so that the passenger is aware of thepotentially dangerous event.

As another example, the internal computing system 110 may detect highwind conditions, which may be difficult for the passenger to understandvisually. Thus, the internal computing system 110 may augment the cameradata feed with computer generated images and information to depict thewind flowing.

In some embodiments, the internal computing system 110 sends, inresponse to determining that a potentially dangerous event is detected,a request for remote assistance from a remote assistance operator. Therequest may include the sensor data, so that the remote assistanceoperator is able to determine, without additional input from thepassenger, the environment around the autonomous vehicle 102. In otherwords, the request may send sensor data to be displayed to remoteassistance, so that remote assistance operator understands thesituation.

At step 210, the internal computing system 110 receives an evaluationdetermining and/or indicating whether the potentially dangerous event isdangerous. In some embodiments, the user or passenger may evaluatewhether the potentially dangerous event is a dangerous event. Similarly,in some embodiments, the remote assistance operator provides theevaluation indicating whether the potentially dangerous event isdangerous, such that the evaluation is received by the internalcomputing system 110 from the remote assistance. Thus, the evaluationmay be used as a built-in redundancy to reduce a number of falsepositive detections of dangerous events by the internal computing system110. In some embodiments, the internal computing system 110 may use thesensor data and the evaluation to train a machine learning algorithmthat determines whether potentially dangerous events are dangerousevents.

At step 212, the internal computing system 110 determines whether thepotentially dangerous event is a dangerous event. As discussed above,the internal computing system 110 may rely on the evaluation todetermine whether the potentially dangerous event is a dangerous event.In some embodiments, the machine learning algorithm may be sufficientlytrained, in that there is a high enough confidence in determiningwhether potentially dangerous events are dangerous, to skip step 210(i.e. receiving the evaluation). In other words, in these embodiments,the internal computing system 110 may determine, based upon the sensordata and the machine learning algorithm, whether the potentiallydangerous event is dangerous.

If the internal computing system 110 determines that the potentiallydangerous event is not a dangerous event, the process 200 returns tostep 202, in which the internal computing system 110 continues receivingsensor data from the sensor systems 104-106 of the autonomous vehicle102.

If the internal computing system 110 determines that the potentiallydangerous event is a dangerous event, the process 200 continues to step214. At step 214, the internal computing system 110 determines whetheremergency services or authorities are necessary. In some embodiments,the evaluation received in step 210 may also determine whether emergencyservices are necessary. Similar to above, in some embodiments, thesensor data and the evaluation may be used to train a machine learningalgorithm to categorize various types of potentially dangerous events.Furthermore, the machine learning algorithm may also be trained to thendetermine whether emergency services are necessary for each type ofpotentially dangerous event. Thus, in these embodiments, the machinelearning algorithm may be sufficiently trained so that there is a highenough confidence in determining whether the event requires emergencyservices.

If the internal computing system 110 determines that emergency servicesare not necessary, the process 200 continues to step 216. At step 216,the internal computing system 110 receives commands through remoteassistance. The commands are effective to allow limited control of theautonomous vehicle 102 from remote assistance. In other words, thecommands allow remote assistance to control the autonomous vehicle 102.For example, remote assistance may determine that a crime is occurringand, while emergency services aren't necessary, the autonomous vehicle102 should be locked, so that the passenger does not disembark directlyinto a crime scene. As another example, remote assistance may navigatean autonomous vehicle 102 that has low tire pressure to pull over.

If the internal computing system 110 determines that emergency servicesare necessary, the process 200 continues to step 218. At step 218, theinternal computing system 110 notifies emergency services about thedangerous event. The internal computing system 110 may also send therelevant sensor data to emergency services to facilitate a faster and/ormore efficient response.

From both step 216 and step 218, the process 200 continues to step 220,in which the internal computing system 110 may send the sensor data, theevaluation, and/or any combination of the various determinations aboveto a database of the remote computing system 150. In some embodiments,the internal computing system 110 may also send the evaluation to theremote computing system 150, which may store the evaluation and therelevant sensor data in a database. The database may then be used forqueries to track incidents and provide additional data and/or evidenceas needed. For example, in the AMBER alert scenario, the images of thevehicle with the specified license plate may be important for trackingand/or evidentiary purposes. Thus, a user may query the database forevaluation and tracking to inform police of sightings, locations, etc.

As discussed above, the sensor data, the evaluation, and/or anycombination of the various determinations above may be used to trainmachine learning algorithms. More specifically, the remote computingsystem 150, using the analysis service 152, may develop these machinelearning algorithms, as described above, to expedite and streamlineprocess 200. For example, the remote computing system 150 may use sensordata and the respective evaluation to train a machine learning algorithmthat determines whether events are potentially dangerous. In someembodiments, this may occur by flagging certain triggers of the eventsthat are associated with being potentially dangerous.

It is further contemplated that the internal computing system 110 mayreceive, prior to detecting the potentially dangerous event, acommunication from the passenger of the autonomous vehicle 102. Thecommunication may indicate that the potentially dangerous event hasoccurred. Thus, the internal computing system 110 may then determine,based on the communication, that a potentially dangerous event hasoccurred and begin implementing the process 200 at step 206.

FIG. 3 shows an example of computing system 300, which can be forexample any computing device making up internal computing system 110,remote computing system 150, (potential) passenger device executingrideshare app 170, or any component thereof in which the components ofthe system are in communication with each other using connection 305.Connection 305 can be a physical connection via a bus, or a directconnection into processor 310, such as in a chipset architecture.Connection 305 can also be a virtual connection, networked connection,or logical connection.

In some embodiments, computing system 300 is a distributed system inwhich the functions described in this disclosure can be distributedwithin a datacenter, multiple data centers, a peer network, etc. In someembodiments, one or more of the described system components representsmany such components each performing some or all of the function forwhich the component is described. In some embodiments, the componentscan be physical or virtual devices.

Example system 300 includes at least one processing unit (CPU orprocessor) 310 and connection 305 that couples various system componentsincluding system memory 315, such as read-only memory (ROM) 320 andrandom access memory (RAM) 325 to processor 310. Computing system 300can include a cache of high-speed memory 312 connected directly with, inclose proximity to, or integrated as part of processor 310.

Processor 310 can include any general purpose processor and a hardwareservice or software service, such as services 332, 334, and 336 storedin storage device 330, configured to control processor 310 as well as aspecial-purpose processor where software instructions are incorporatedinto the actual processor design. Processor 310 may essentially be acompletely self-contained computing system, containing multiple cores orprocessors, a bus, memory controller, cache, etc. A multi-core processormay be symmetric or asymmetric.

To enable user interaction, computing system 300 includes an inputdevice 345, which can represent any number of input mechanisms, such asa microphone for speech, a touch-sensitive screen for gesture orgraphical input, keyboard, mouse, motion input, speech, etc. Computingsystem 300 can also include output device 335, which can be one or moreof a number of output mechanisms known to those of skill in the art. Insome instances, multimodal systems can enable a user to provide multipletypes of input/output to communicate with computing system 300.Computing system 300 can include communications interface 340, which cangenerally govern and manage the user input and system output. There isno restriction on operating on any particular hardware arrangement, andtherefore the basic features here may easily be substituted for improvedhardware or firmware arrangements as they are developed.

Storage device 330 can be a non-volatile memory device and can be a harddisk or other types of computer readable media which can store data thatare accessible by a computer, such as magnetic cassettes, flash memorycards, solid state memory devices, digital versatile disks, cartridges,random access memories (RAMs), read-only memory (ROM), and/or somecombination of these devices.

The storage device 330 can include software services, servers, services,etc., that when the code that defines such software is executed by theprocessor 310, it causes the system to perform a function. In someembodiments, a hardware service that performs a particular function caninclude the software component stored in a computer-readable medium inconnection with the necessary hardware components, such as processor310, connection 305, output device 335, etc., to carry out the function.

For clarity of explanation, in some instances, the present technologymay be presented as including individual functional blocks includingfunctional blocks comprising devices, device components, steps orroutines in a method embodied in software, or combinations of hardwareand software.

Any of the steps, operations, functions, or processes described hereinmay be performed or implemented by a combination of hardware andsoftware services or services, alone or in combination with otherdevices. In some embodiments, a service can be software that resides inmemory of a client device and/or one or more servers of a contentmanagement system and perform one or more functions when a processorexecutes the software associated with the service. In some embodiments,a service is a program or a collection of programs that carry out aspecific function. In some embodiments, a service can be considered aserver. The memory can be a non-transitory computer-readable medium.

In some embodiments, the computer-readable storage devices, mediums, andmemories can include a cable or wireless signal containing a bit streamand the like. However, when mentioned, non-transitory computer-readablestorage media expressly exclude media such as energy, carrier signals,electromagnetic waves, and signals per se.

Methods according to the above-described examples can be implementedusing computer-executable instructions that are stored or otherwiseavailable from computer-readable media. Such instructions can comprise,for example, instructions and data which cause or otherwise configure ageneral purpose computer, special purpose computer, or special purposeprocessing device to perform a certain function or group of functions.Portions of computer resources used can be accessible over a network.The executable computer instructions may be, for example, binaries,intermediate format instructions such as assembly language, firmware, orsource code. Examples of computer-readable media that may be used tostore instructions, information used, and/or information created duringmethods according to described examples include magnetic or opticaldisks, solid-state memory devices, flash memory, USB devices providedwith non-volatile memory, networked storage devices, and so on.

Devices implementing methods according to these disclosures can comprisehardware, firmware and/or software, and can take any of a variety ofform factors. Typical examples of such form factors include servers,laptops, smartphones, small form factor personal computers, personaldigital assistants, and so on. The functionality described herein alsocan be embodied in peripherals or add-in cards. Such functionality canalso be implemented on a circuit board among different chips ordifferent processes executing in a single device, by way of furtherexample.

The instructions, media for conveying such instructions, computingresources for executing them, and other structures for supporting suchcomputing resources are means for providing the functions described inthese disclosures.

Although a variety of examples and other information was used to explainaspects within the scope of the appended claims, no limitation of theclaims should be implied based on particular features or arrangements insuch examples, as one of ordinary skill would be able to use theseexamples to derive a wide variety of implementations. Further andalthough some subject matter may have been described in languagespecific to examples of structural features and/or method steps, it isto be understood that the subject matter defined in the appended claimsis not necessarily limited to these described features or acts. Forexample, such functionality can be distributed differently or performedin components other than those identified herein. Rather, the describedfeatures and steps are disclosed as examples of components of systemsand methods within the scope of the appended claims.

What is claimed is:
 1. A method for dynamically providing sensor data obtained from a relevant sensor of a plurality of sensors to a user associated with an autonomous vehicle based on an environment around the autonomous vehicle, the method comprising: receiving the plurality of sensor data from the plurality of sensors disposed on the autonomous vehicle, wherein the plurality of sensor data comprises measurements associated with the environment around the autonomous vehicle; determining, based on the plurality of sensor data, an earthquake has occurred in the environment around the autonomous vehicle; determining, in response to determining that the earthquake has occurred in the environment around the autonomous vehicle and based upon sensor data that is associated with determining that the earthquake has occurred in the environment, a relevant sensor of the plurality of sensors, wherein the relevant sensor is a suspension system of the autonomous vehicle; and displaying, in response to determining that the earthquake has occurred in the environment, the sensor data obtained from the relevant sensor, wherein displaying the sensor data obtained from the relevant sensor includes computer generated images and information that augment the sensor data obtained from the relevant sensor.
 2. The method of claim 1, further comprising: sending, in response to determining that the earthquake has occurred in the environment around the autonomous vehicle, a request for remote assistance, the request further including at least the portion of the sensor data obtained from the relevant sensor; and receiving an evaluation indicating a determination of an extent of damage or danger caused by the earthquake.
 3. The method of claim 1, further comprising: receiving commands through remote assistance, the commands effective to allow limited control of the autonomous vehicle.
 4. The method of claim 1, further comprising: receiving an alert communicating that the earthquake has occurred in the environment around the autonomous vehicle; notifying, upon determining that the earthquake has occurred in the environment around the autonomous vehicle, emergency services or authorities; and sending, to the emergency services or authorities, the sensor data obtained from the relevant sensor.
 5. The method of claim 1, further comprising: sending, to a remote computing system, the sensor data obtained from the relevant sensor, wherein the sensor data obtained from the relevant sensor is used to train a machine learning algorithm that determines whether an impact of the earthquake is dangerous.
 6. The method of claim 1, further comprising: receiving communicated data from a remote computing system, wherein determining that the earthquake has occurred in the environment around the autonomous vehicle is further based on the communicated data.
 7. The method of claim 1, further comprising: receiving, prior to determining that the earthquake has occurred in the environment around the autonomous vehicle, a communication from a passenger of the autonomous vehicle, the communication indicating that the earthquake has occurred.
 8. A non-transitory computer readable medium for dynamically providing sensor data obtained from a relevant sensor of a plurality of sensors to a user associated with an autonomous vehicle based on an environment around the autonomous vehicle, the non-transitory computer readable medium comprising instructions stored thereon, the instructions effective to cause at least one processor to: receive the plurality of sensor data from a plurality of sensors disposed on the autonomous vehicle, wherein the plurality of sensor data comprises measurements associated with the environment around the autonomous vehicle; determine, based on the plurality of sensor data, that an earthquake has occurred in the environment around the autonomous vehicle; determine, in response to determining that determining that the earthquake has occurred in the environment around the autonomous vehicle and based upon sensor data that is associated with determining that the earthquake has occurred in the environment, a relevant sensor of the plurality of sensors, wherein the relevant sensor is a suspension system of the autonomous vehicle; and display, in response to determining that the earthquake has occurred in the environment, the sensor data obtained from the relevant sensor, wherein displaying the sensor data obtained from the relevant sensor includes computer generated images and information that augment the sensor data obtained from the relevant sensor.
 9. The non-transitory computer readable medium of claim 8, wherein the instructions are further effective to cause the at least one processor to: send, in response to determining that the earthquake has occurred in the environment around the autonomous vehicle, a request for remote assistance, the request further including at least the portion of the sensor data obtained from the relevant sensor; and receive an evaluation indicating a determination of an extent of damage or danger caused by the earthquake.
 10. The non-transitory computer readable medium of claim 8, wherein the instructions are further effective to cause the at least one processor to: receive commands through remote assistance, the commands effective to allow limited control of the autonomous vehicle.
 11. The non-transitory computer readable medium of claim 8, wherein the instructions are further effective to cause the at least one processor to: receive communicated data from a remote computing system, wherein determining that the earthquake has occurred in the environment around the autonomous vehicle is further based on the communicated data.
 12. The non-transitory computer readable medium of claim 8, wherein the instructions are further effective to cause the at least one processor to: receive, prior to determining that the earthquake has occurred in the environment around the autonomous vehicle, a communication from a passenger of the autonomous vehicle, the communication indicating that the earthquake has occurred.
 13. A system for dynamically providing sensor data obtained from a relevant sensor of a plurality of sensors to a user associated with an autonomous vehicle based on an environment around the autonomous vehicle, the system comprising: at least one processor; at least one memory storing computer-readable instructions that, when executed by the at least one processor, causes the at least one processor to: receive the plurality of sensor data from the plurality of sensors disposed on the autonomous vehicle, wherein the plurality of sensor data comprises measurements associated with the environment around the autonomous vehicle; determine, based on the plurality of sensor data, an earthquake has occurred in the environment around the autonomous vehicle; determine, in response to determining that the earthquake has occurred in the environment around the autonomous vehicle and based upon sensor data that is associated with determining that the earthquake has occurred in the environment around the autonomous vehicle, a relevant sensor of the plurality of sensors, wherein the relevant sensor is a suspension system of the autonomous vehicle; and display, in response to determining that the earthquake has occurred in the environment, the sensor data obtained from the relevant sensor, wherein displaying the sensor data obtained from the relevant sensor includes computer generated images and information that augment the sensor data obtained from the relevant sensor.
 14. The system of claim 13, wherein the instructions are effective to further cause the at least one processor to: send, in response to determining that the earthquake has occurred in the environment around the autonomous vehicle, a request for remote assistance, the request further including at least the portion of the sensor data obtained from the relevant sensor; and receive an evaluation indicating a determination of an extent of damage or danger caused by the earthquake.
 15. The system of claim 13, wherein the instructions are effective to further cause the at least one processor to: receive commands through remote assistance, the commands effective to allow limited control of the autonomous vehicle.
 16. The system of claim 13, wherein the instructions are effective to further cause the at least one processor to: receive communicated data from a remote computing system, wherein determining that the earthquake has occurred in the environment around the autonomous vehicle is further based on the communicated data.
 17. The system of claim 13, wherein the instructions are effective to further cause the at least one processor to: receive, prior to determining that the earthquake has occurred in the environment around the autonomous vehicle, a communication from a passenger of the autonomous vehicle, the communication indicating that the earthquake has occurred. 